Robust adaptive brain stimulation

ABSTRACT

Systems and methods that automatically adjust, or adapt, stimulation waveforms delivered to brain structures. Closed loop system embodiments can automatically be reconfigured into a more suitable closed loop control system in response to measures of control system performance. Measures can be internal performance characteristics of the adaptive control system or external inputs provided by another subsystem. As these measures change in time, the robust adaptive system changes in response.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 63/199,069, filed Dec. 4, 2020, the disclosure of which is herebyincorporated by reference in its entirety.

FIELD

The present technology is generally related to medical devices and, moreparticularly, medical devices that deliver adaptive brain stimulationtherapy.

BACKGROUND

Patients with neurodegenerative diseases or trauma like cerebral infarctor spinal cord injury can have a variety of movement and muscle controlproblems, like resting, postural, intention or action tremor; dystonia(improper muscle tone, myoclonus); spasticity (undesirable movements, ormuscle co-contraction); dyskinesia (poorly executed movements) orinvoluntary movements like ballismus, choreiform movements andtorticollis (inappropriate movements or limb control). Many of theseproblems can be called hyperkinesia. Although they can be chronic, orworse, progressive, they also may have times of relative remission. Suchproblems are found, at certain stages, for patients with Parkinson'sdisease, multiple sclerosis, cerebral palsy, secondary todeafferentation pain, post stroke, post apoplexy or anoxia, post head orspinal trauma, post poisoning, cerebellar disease, etc. Dyskinesia alsomay result from long term usage of L-dopa, or Levodopa, for Parkinson'spatients, or other drugs.

Medical devices that provide deep brain stimulation (DBS) of certainbrain structures can alleviate, diminish, or completely stop symptoms ofthe aforementioned diseases or traumas. Traditionally, DBS therapy wasprovided as a continuous train of pulses. However, more recent advanceshave adapted or regulated the stimulation delivered to the brain, forexample, by getting physiological feedback and adjusting the therapy,often against threshold values.

However, patient states in a disease can change week-to-week orday-to-day or even by hour as medications or circadian rhythms changesymptoms. Further, diseases can progress over time. Moreover, it can bedifficult to set appropriate forward-looking threshold values at anygiven point in time (e.g. at the clinic). And, changing those thresholdsoften requires an additional clinic visit. Accordingly, a constanttherapy or even threshold-adjusted therapy is not always appropriate.Further, a one-size-fits-all algorithm or set of algorithms that DBSdevices implement may not be optimal.

Therefore, there is a need to adapt the adaptive DBS therapiesthemselves.

SUMMARY

The techniques of this disclosure generally relate to embodiments thatautomatically adjust, or adapt, stimulation waveforms delivered to brainstructures. Adaptive brain stimulation systems include a measurement andan actuation system. For example, a local field potential sensor, and/ormovement sensor is used in a measurement system, and in coordination, anactuation system can include a stimulation waveform adjustment to aparameter such as amplitude, pulse width, rate, location (electrodeconfiguration) or some combination therein. The measurement systemprovides real-time feedback to the actuation system, as in theclassically defined closed-loop control system. Embodiments describedherein include an adaptive brain stimulation system, which itself canadapt. Moreover, closed loop system embodiments can automatically bere-configured into a more suitable closed loop control system inresponse to measures of control system performance. Measures can includeinternal performance characteristics of the adaptive control system(e.g. dwell time/entropy in a particular state, actuator saturation,actuator oscillation/stability), or external inputs provided by anothersubsystem (e.g. poor symptom control, undesired side effects). As thesemeasures change in time, the robust adaptive system changes in response.

In an embodiment, the disclosure describes an adaptive deep brainstimulation system comprising at least one processor and memory operablycoupled to the at least one processor, a measurement subsystem includinga plurality of sensing contacts, and measurement subsystem instructionsthat, when executed on the at least one processor, cause the processorto implement a first signal classification algorithm using one or moreof the plurality of sensing contacts to generate a first plurality ofsignals of interest, an actuation subsystem including a plurality ofstimulation contacts and actuation subsystem instructions that, whenexecuted on the at least one processor, cause the processor to providetherapy on one or more of the plurality of stimulation contacts by oneor more actuators according to a first control policy and a firstcontrol parameter, a reconfiguration subsystem including reconfigurationsubsystem instructions that, when executed on the at least oneprocessor, cause the processor to determine at least one performancemeasure related to the measurement subsystem or the actuation subsystem,detect a change in performance of the at least one performance measureagainst a performance threshold, and based on the detecting, generateupdated measurement subsystem instructions, wherein the updatedmeasurement subsystem instructions includes a second signalclassification algorithm or a second plurality of signals of interest,or generate updated actuation subsystem instructions, wherein theupdated actuation subsystem instructions includes a second controlpolicy or a second control parameter, and store at least one of theupdated measurement subsystem instructions or the updated actuationsubsystem instructions in the memory, wherein the updated measurementsubsystem instructions or the updated actuation subsystem instructions,when executed on the at least one processor, cause the processor toprovide an updated therapy on the one or more of the plurality ofstimulation contacts.

In an embodiment, the disclosure describes a method for adapting anadaptive deep brain stimulation system including at least one processorand memory operably coupled to the at least one processor, a measurementsubsystem including a plurality of sensing contacts, and measurementsubsystem instructions that, when executed on the at least oneprocessor, cause the processor to implement a first signalclassification algorithm using one or more of the plurality of sensingcontacts to generate a first plurality of signals of interest, and anactuation subsystem including a plurality of stimulation contacts, andactuation subsystem instructions that, when executed on the at least oneprocessor, cause the processor to provide therapy on one or more of theplurality of stimulation contacts by one or more actuators according toa first control policy and a first control parameter, the methodcomprising determining at least one performance measure related to themeasurement subsystem or the actuation subsystem, detecting a change inperformance of the at least one performance measure against aperformance threshold, and based on the detecting, generating updatedmeasurement subsystem instructions, wherein the updated measurementsubsystem instructions includes a second signal classification algorithmor a second plurality of signals of interest, or generating updatedactuation subsystem instructions, wherein the updated actuationsubsystem instructions includes a second control policy or a secondcontrol parameter, storing at least one of the updated measurementsubsystem instructions or the updated actuation subsystem instructionsin the memory, wherein the updated measurement subsystem instructions orthe updated actuation subsystem instructions, when executed on the atleast one processor, cause the processor to provide an updated therapyon the one or more of the plurality of stimulation contacts.

The details of one or more aspects of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the techniques described in this disclosurewill be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example system thatprovides robust adaptive brain stimulation, according to an embodiment.

FIG. 2 is a conceptual diagram illustrating an example system thatprovides robust adaptive brain stimulation, according to an embodiment.

FIG. 3 is a block diagram of a system, including various engines, thatprovides robust adaptive brain stimulation, according to an embodiment.

FIG. 4 is a combination block diagram and flowchart of a system thatprovides robust adaptive brain stimulation, according to an embodiment.

FIG. 5 is a combination block diagram and flowchart of a system thatprovides robust adaptive brain stimulation, according to an embodiment.

FIG. 6 is a block diagram of a system that provides robust adaptivebrain stimulation in view of multiple actuators, according to anembodiment.

FIG. 7 is a block diagram of a system that provides robust adaptivebrain stimulation in view of dwell time, according to an embodiment.

FIG. 8 is a block diagram of a system that provides robust adaptivebrain stimulation in view of therapy duty cycle, according to anembodiment.

FIG. 9 is a block diagram of a system that provides robust adaptivebrain stimulation in view of actuation speed, according to anembodiment.

DETAILED DESCRIPTION

Referring to FIG. 1, a conceptual diagram illustrating an example system100 that provides robust adaptive brain stimulation is depicted,according to an embodiment. In an embodiment, system 100 comprises animplantable medical device (IMD) 102 for a patient 104.

IMD 102 delivers neurostimulation therapy to patient 104 at a targettissue site. For example, one or more leads may extend from IMD 102 tothe brain of patient 104, and IMD 102 may deliver deep brain stimulation(DBS) therapy to patient 104 to, for example, treat the aforementionedneurodegenerative diseases or traumas.

IMD 102 is configured to deliver therapy according to one or moreprograms or control policies. A control policy includes one or moreparameters that define an aspect of the therapy delivered by the medicaldevice according to that control policy. For example, a control policythat controls delivery of stimulation by IMD 102 in the form of pulsesmay define a voltage or current pulse amplitude, a pulse width, a pulserate, an electrode configuration or field shape, or a schedule orpattern for stimulation pulses delivered by IMD 102 according to thatcontrol policy. Further, each of the leads includes electrodes, and theparameters for a control policy that controls delivery of stimulationtherapy by IMD 102 can include information identifying which electrodeshave been selected for delivery of pulses according to the program, andthe polarities of the selected electrodes, i.e., the electrodeconfiguration for the control policy. Control policies that controldelivery of other therapies by IMD 102 can include other parameters, asrequired by the particular policy.

Optionally, in an embodiment, as depicted, system 100 further comprisesan external device 106. External device 106 is communicatively coupledto IMD 102 via wireless communication. In embodiments, external device106 can be a device for inputting information relating to patient 104,receiving information from IMD 102, and updating IMD 102 according tothe robust adaptive algorithms and methods described herein.

Referring also to FIG. 2, a conceptual diagram illustrating an examplesystem that provides robust adaptive brain stimulation is depicted,according to an embodiment.

As depicted, external devices 106 a and 106 b are further illustrated asa handheld computing device and a laptop computing device, but canfurther be a key fob or a wristwatch, smart phone, computer workstation,or networked computing device, and the like.

Embodiments of systems can further include a server 108 and/or adatabase 110, as illustrated.

Server 108 can include one or more servers in a cloud computingenvironment. Server 108 can be configured to communicate with externaldevices 106 a and/or 106 b, and/or IMD 102 via wireless communication.In embodiments, server 108 can be co-located with external devices 106 aand/or 106 b, or located elsewhere, such as in a cloud computing datacenter or medical clinic.

Database 110 is configured to store data related to system 100,including IMD 102 and/or external device 106 data. In embodiments,database 110 can be integrated as part of server 108, or be standalonesuch that server 110 and/or external device 106 a and 106 b can becommunicatively coupled to database 110.

Referring to FIG. 3, a block diagram of a system 200, including variousengines, that provides robust adaptive brain stimulation is depicted,according to an embodiment.

System 200 generally comprises at least one processor 202 and memory 204for implementing engines measurement engine 206, actuation engine 208,and reconfiguration engine 210.

The various engines or tools are constructed, programmed, configured, orotherwise adapted, to autonomously carry out a function or set offunctions. The term engine as used herein is defined as a real-worlddevice, component, or arrangement of components implemented usinghardware, such as by an application specific integrated circuit (ASIC)or field-programmable gate array (FPGA), for example, or as acombination of hardware and software, such as by a microprocessor systemand a set of program instructions that adapt the engine to implement theparticular functionality, which (while being executed) transform themicroprocessor system into a special-purpose device. An engine can alsobe implemented as a combination of the two, with certain functionsfacilitated by hardware alone, and other functions facilitated by acombination of hardware and software. In certain implementations, atleast a portion, and in some cases, all, of an engine can be executed onthe processor(s) of one or more computing platforms that are made up ofhardware (e.g., one or more processors, data storage devices such asmemory or drive storage, input/output facilities such as networkinterface devices, video devices, keyboard, mouse or touchscreendevices, etc.) that execute an operating system, system programs, andapplication programs, while also implementing the engine usingmultitasking, multithreading, distributed (e.g., cluster, peer-peer,cloud, etc.) processing where appropriate, or other such techniques.Accordingly, each engine can be realized in a variety of physicallyrealizable configurations, and should generally not be limited to anyparticular implementation exemplified herein, unless such limitationsare expressly called out. In addition, an engine can itself be composedof more than one sub-engines, each of which can be regarded as an enginein its own right. Moreover, in the embodiments described herein, each ofthe various engines corresponds to a defined autonomous functionality;however, it should be understood that in other contemplated embodiments,each functionality can be distributed to more than one engine. Likewise,in other contemplated embodiments, multiple defined functionalities maybe implemented by a single engine that performs those multiplefunctions, possibly alongside other functions, or distributeddifferently among a set of engines than specifically illustrated in theexamples herein.

Processor 202 can be any programmable device that accepts digital dataas input, is configured to process the input according to instructionsor algorithms, and provides results as outputs. In an embodiment,processor 202 can be a central processing unit (CPU) configured to carryout the instructions of a computer program. Processor 202 is thereforeconfigured to perform at least basic arithmetical, logical, andinput/output operations. Processor 202 can be a microprocessor, acontroller, a digital signal processor (DSP), ASIC, FPGA, discrete logiccircuitry, or the like.

Memory 204 can comprise volatile or non-volatile memory as required bythe coupled processor 202 to not only provide space to execute theinstructions or algorithms, but to provide the space to store theinstructions themselves. Memory 204 can include program instructionsthat, when executed by processor 202, cause the systems to implementedthe various functions described herein. Memory 204 can include anyvolatile, non-volatile, magnetic, optical, or electrical media, such asa random access memory (RAM), read-only memory (ROM), non-volatile RAM(NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory,ferroelectric RAM, hard disk, floppy disk, magnetic tape, or opticaldisc storage, for example. The foregoing lists in no way limit the typeof memory that can be used, as these embodiments are given only by wayof example and are not intended to limit the scope of embodiments.

Measurement engine 206 is configured to operate on one or more signalsof interest, according to a signal classification algorithm. Forexample, measurement engine 206 can include or otherwise utilize asensor such as a local field potential sensor, chemical sensor, and/ormovement sensor in coordination with a signal classification algorithmto determine characteristics related to one or more signals of interestmeasured by the sensor. In an embodiment, the sensor senses a patientparameter, and values of the patient parameter can be determined (suchas by processor 202) based on the signal generated by the sensor as afunction of the patient parameter.

Actuation engine 208 is configured with at least one control parameterand a control policy to generate a stimulation waveform adjustment tothe control parameter such as amplitude, pulse width, rate, location,electrode configuration and field shape, pattern, schedule or somecombination therein. Accordingly, actuation engine 208 can include orotherwise utilize stimulation contacts, leads, electrodes, and so on, ofIMD 102.

Reconfiguration engine 210 is configured to dynamically adjust one ormore components of measurement engine 206 and/or actuation engine 208.In an embodiment, reconfiguration engine 210 can utilize internalperformance characteristics of the control system (e.g. dwelltime/entropy in a particular state, actuator saturation, actuatoroscillation/stability, etc.), or external inputs provided by anothersubsystem (e.g. poor symptom control, undesired side effects, etc.) toadjust one or more components of measurement engine 206 and/or actuationengine 208.

Accordingly, the components of system 200 can be implemented wholly aspart of or internal to IMD 102. In other embodiments, the components ofsystem 200 can be implemented wholly or partially external to IMD 102such that a governor overseer such as external device 106, 106 a, or 106b can update the functionality of IMD 102. In other embodiments, thecomponents of system 200 can be implemented as a highly distributedsystem over a cloud and/or server network.

Referring to FIG. 4, a combination block diagram and flowchart of asystem 300 that provides robust adaptive brain stimulation is depicted,according to an embodiment. System 300 generally comprises an adaptivebrain stimulation system 302 and components to reconfigure adaptivebrain stimulation system 302.

More particularly, system 300 is configured for reconfiguration ofadaptive brain stimulation system 302, which itself can adapt. Adaptivebrain stimulation system 302 generally comprises a measurement system(or subsystem) 304 and an actuation system (or subsystem) 306. Thoughnot shown, it is readily understood by one of ordinary skill in the artthat actuation system 302 can adapt or regulate the stimulationdelivered to the brain, for example, by getting physiological feedbackand adjusting the therapy based on the physiological feedback comparedto a threshold value.

In an embodiment, components depicted outside of adaptive brainstimulation system 302 can be included in a reconfiguration subsystem(not labelled for ease of illustration). The reconfiguration of adaptivebrain stimulation system 302 is accordingly not mere adjustments oftherapy based on a physiological feedback comparison against a thresholdvalue.

Rather, in operation, at 308, system 300 senses, obtains, or otherwiseretrieves at least one internal performance measure of adaptive brainstimulation system 302.

At 310, an analysis or evaluation is conducted as to whether there is achange in performance of adaptive brain stimulation system 302 based onthe inputted at least one internal performance measure.

At 312, based on the analysis at 310, there is no change detected inperformance of adaptive brain stimulation system 302 and system 300 isdetermined to be optimal.

However, at 314, if a change in performance of adaptive brainstimulation system 302 is detected, adaptive brain stimulation system302 can be reconfigured. In an embodiment, as illustrated, a change toone or more signals of interest 316, a signal classification algorithm318, a control parameter 320, and/or a control policy 322 can be made.

In an embodiment, one or more signals of interest 316 can includepatient LFP signals (frequency and amplitude), patient kinematic signals(e.g. wearable sensor rotational rate), and/or patient symptom/sideeffect sensor signals (e.g. visual sensor classifying patient movement).

For example, for electrophysiological local field potentials,embodiments can include the variation of the signal over time, orspecific features (bursts, spikes). Embodiments can further includederived properties of the signal, such as power in a specific frequencyband (beta power). Embodiments can further include changes in eithertime or power behavior (bursting in beta). Embodiment can includerelationships between two LFP signals (coherence, synchrony,dysynchrony).

In another example, patient kinematic signals can be wearable measuresof movement, movement quality, symptom specific measures like tremordetection, overall activity/rest measures, gait or gait quality, orevents such as falls.

In another example, patient symptoms can be reported by the patient orcan be detected by external systems (e.g. video analysis). Patientsymptoms can further include items like medication markers (reported ordetected by a smart pill box).

In further examples, one or more signals of interest 316 can furtherinclude chemical sensors of physiological markers (neurotransmitters,metabolic byproducts) or of drug levels in the body (l-dopa levels, ormarkers of l-dopa breakdown), cardiac heart rate, respiration, sleepmetrics, blood sugar, and/or temperature.

In an embodiment, signal classification algorithm 318 can include anyalgorithm used to provide a clinical context to signal of interest(s)316 either in a continuous mode or discrete mode. For example, in acontinuous mode: a real time error in a signal of interest compared to adesired target value. In a discrete mode: classifying the signal ofinterest into predefined bins of values such as (Low/Middle/High), etc.In another example, signal classification algorithm 318 can includediscrete measures such as (above/within/below).

In another embodiment, signal classification algorithm 318 can includederived measures like variance or rate of change, rising/falling,stability or trend metrics.

In another embodiment, embodiments of signal classification algorithm318 can include or implement event or state detection; for example (preseizure/in seizure/post seizure), (rigid/dyskinetic), (sleep/wake).

In an embodiment, control parameter 320 can include any parameterrelated to selection of one or more signal of interest 316.

In an embodiment, control policy 322 can include any mathematicaldescription that relates control parameters and the output of theclassification algorithm to the actuator response. For example, anincrease/decrease in stimulation output in response to adecrease/increase in a particular, or combination of, controlparameter(s) and their classified output. Such examples can be dualthreshold single threshold or single threshold inverse.

In embodiments, control policy 322 can be utilized for patternswitching. As described herein, embodiments are configured to anticipatepatterns of stimulation that might be delivered at the same amplitudebut which are designed in their pulse sequences to have a greater orlesser desynchronizing effect on SOI. Accordingly, pattern switching isutilized as control policy 322 output and/or target for adaptation.

In an embodiment, similar to the aforementioned determinations based oninternal performance measures, external/subsystem measures can beobtained at 324. External subsystem measures can be input to 310 foranalysis or evaluation as to whether there is a change in performance ofadaptive brain stimulation system 302. Evaluations using internalperformance measures 308 can be separate or discrete from evaluationsusing external/subsystem measures 324. In other embodiments, evaluationscan utilize both internal performance measures 308 andexternal/subsystem measures 324 to determine optimal or suboptimalconfigurations.

Referring to FIG. 5, a combination block diagram and flowchart of asystem 400 that provides robust adaptive brain stimulation is depicted,according to an embodiment. Similar to FIG. 4, system 400 is configuredfor reconfiguration of adaptive brain stimulation system 402. System 400generally comprises an adaptive brain stimulation system 402 andcomponents to reconfigure adaptive brain stimulation system 402.

More particularly, system 400 is configured for reconfiguration ofadaptive brain stimulation system 402, which itself can adapt. Adaptivebrain stimulation system 402 generally comprises a measurement system(or subsystem) 404 and an actuation system (or subsystem) 406. In anembodiment, components depicted outside of adaptive brain stimulationsystem 402 can be included in a reconfiguration subsystem.

In an embodiment, measurement system 404 generally comprises one or moresignals of interest 408 and a signal classification algorithm 410. In anembodiment, actuation system 406 generally comprises one or more controlparameters 412 and a control policy 414.

In operation, reconfiguration subsystem can utilize performance measures416 to reconfigure 418 adaptive brain stimulation system 402.

In an embodiment, reconfigure at 418 includes adding a new state to anexisting set of control states. For example, when it is determined thatthe existing control states/thresholds are not able to account for newlyarising conditions, a new state can be added. As an example, a patientinitially with two states (On/Off PD symptoms) might develop a thirdstate over time (On with dyskinesias) that is not simply an extension ofthe On state thresholds, but requires separate management.

The examples of performance measures and reconfiguration provided inFIG. 5 are provided by way of example only, and are in no way limiting.

In an embodiment, internal performance measures are determined based oninputs from adaptive brain stimulation system 402 and analyzed at 416.In an embodiment, external performance measures 436 are determined basedon inputs from external to adaptive brain stimulation system 402 andanalyzed at 416.

At 420, saturation of a control parameter is detected. Accordingly, at422, an adjustment to the control parameter limit or selection of adifferent or additional control parameter is made. As illustrated, suchadjustments reconfigure control parameter 412.

At 424, classification dwelling in a particular state is detected at424. In embodiments, the states can be predefined or dynamically definedbased on the therapy. Accordingly, at 426, an adjustment to theclassification thresholds 426 is made. As illustrated, such adjustmentsreconfigure signal classification algorithm 410.

At 428, a side effect is measured. Accordingly, at 430, the side effectcan be added to signal of interest 408. As illustrated, such adjustmentsreconfigure signal of interest 408.

At 432, a transient inadequate symptom control is measured. Accordingly,at 434, control policy ephemeral parameters are adjusted. Asillustrated, such adjustments reconfigure control policy 414.

Additional embodiments are described herein with respect to FIGS. 6-9.The systems depicted and described by these figures can be implementedby the components of FIGS. 1-5, as will readily be understood by one ofordinary skill in the art.

Referring to FIG. 6, a block diagram of a system 500 that providesrobust adaptive brain stimulation in view of multiple actuators to apatient brain 502 is depicted, according to an embodiment. System 500 isconfigured to be robust to control actuator saturation.

In an embodiment, system 500 comprises a plurality of sensing contacts506, a first switch matrix 508, a plurality of signals of interest 510generated according to a plurality of channels and a digital signalprocessing, a second switch matrix 512, an adaptive state machine 514,actuator selector logic 516, a third switch matrix 518, and a pluralityof stimulation contacts 520.

In operation, at least one of the plurality of sensing contacts 506 areinput to first switch matrix 508, which is configured to down-select oneor more of the plurality of sensing contacts 506. Based on theselection, inputs to the respective channels is made, and subsequentsignal processing is conducted to generate the plurality of signals ofinterest 510. At least one of the signals of interest 510 are input tosecond switch matrix 512, which is configured to down-select inputs toadaptive state machine 514.

Adaptive state machine 514 is configured to control at least one signalof interest to a desired target using a selected control policy. Inembodiments, adaptive state machine is further configured to control atleast one signal of interest to a desired target using a selectedactuator.

From adaptive state machine 514, inputs to actuator selector logic 516are made. Actuator selector logic 516 is configured to detect a currentactuator state and classify the actuator as “high”, “in range”, or“low”. Based on the actuator classification, an input to third switchmatrix 518 is made. Third switch matrix 518 is configured to selectamong the plurality of stimulation contacts 520 for interaction withpatient brain 502.

In an embodiment, a new adaptive state machine can be swapped inentirely. For example, based on detected performance changes, adaptivestate machine 514 can include a set of logic blocks, wherein theactuator feedback can attempt to optimize within a first adaptive statemachine, but then switch to an entirely different second adaptive statemachine when it becomes clear a new state has been entered. As anexample, the control policy for preventing a seizure might look sodifferent to that for responding to/aborting a seizure that it is betterthought of as a whole new regime.

In robust adaptive operation, a clinician defines the operating space asan input 504 to system 500. In an embodiment, the clinician defines notonly a clinically relevant stimulation amplitude operating space, butalso stimulation pulse width operating space, and stimulation electrodesthat are therapeutic. In an embodiment, safety limits such as thosedefined by the clinician are important to systems operating on the brainover general control system theory.

System 500 controls the signal of interest using a defined controlpolicy (e.g. dual threshold, single threshold, single threshold inverse,etc.). Further, embodiments also monitor the state of the actuators thatare in use. If a particular actuator becomes saturated in the upperlimit of that actuators operating space, another actuator is recruitedfor use, as defined by the clinician's priority of actuation preference(e.g. from input 504). Likewise, if additional actuators have beenrecruited, and at a later point in time the additional actuatorssaturate in the lower limit of their operating space, the saturatedactuators are removed from the actuation system.

In an embodiment, when actuator selector logic 516 determines anactuator state to be high, additional actuators can be recruited in apriority order, such as that illustrated—increase stimulation amplitude,stimulation pulse width, then stimulation electrodes. In an embodiment,when actuator selector logic 516 determines an actuator state to be low(in which the stimulation actuators likely need to be turned down),actuators are removed in a priority order, such as thatillustrated—stimulation electrodes, stimulation pulse width, thenstimulation amplitude. When actuator selector logic 516 determines anactuator state to be in range, no actuators are added or removed. Asdepicted, actuator feedback can thus be input back to adaptive statemachine 514. Accordingly, robustness comes from recruiting orunrecruiting additional actuators. In embodiments, a clinician canchoose the order of relative addition or removal, or the relative ordercan be predefined.

Referring to FIG. 7, a block diagram of a system 600 that providesrobust adaptive brain stimulation in view of dwell time to a patientbrain 602 is depicted, according to an embodiment.

In an embodiment, system 600 can have substantially similar componentsto system 500, which are not described again here for conciseness. Forexample, system 600 can operate on clinician input 604 and comprises aplurality of sensing contacts 606, a first switch matrix 508, aplurality of signals of interest 610 generated according to a pluralityof channels and a digital signal processing, a second switch matrix 612,an adaptive state machine 614, a third switch matrix 618, and aplurality of stimulation contacts 620.

As depicted, system 600 can further include detector thresholdadjustment logic 616 and 617.

As further depicted, adaptive state machine 614 is configured toclassify a signal of interest target based upon thresholds defined inthreshold adjustment logic 616 and 617. Adaptive state machine 614 isfurther configured to control a signal of interest using a proportionalcontrol policy. As depicted, threshold feedback can thus be input backto adaptive state machine 614.

In robust adaptive operation, system 600 is robust to SOI thresholdschanging over time, or improperly configured to begin with. Theclinician defines (e.g. input 604) the stimulation amplitude limits fora proportional control policy. The clinician also defines a signal ofinterest (SOI) threshold upon which system 600 acts. In an embodiment,system 600 assumes that the clinician-defined stimulation operatingspace is a clinical constraint, and that system 600 should be adaptingstimulation within this envelope, but isn't adapting.

In an embodiment, one or more timers are utilized; for example, a timerthat tracks when the controller stops adjusting stimulation due to SOIclassification being unchanged. The timer can slowly increment thedetector classification of the signal of interest, until the underlyingSOI is classified such that stimulation will adapt. Such operation iseffective for both over-classified and under-classified signals ofinterest.

More particularly, referring to threshold adjustment logic 616, a gridof current actuator state against current detector state is depicted fordwell durations “low”, “in range”, and “high”. Corresponding dwell timeroperation is illustrated based on the actuator and detector states. Thegrid accordingly operates on the respective timers based on the timesspent above or below the thresholds, in comparison to other inputs.

In an embodiment, for a proportional controller, a low detector inputwould require low actuation, and a high detector input would requirehigh actuation such that the timers in the grid increment only when inthis low/low state or high/high state.

Accordingly, at 617, if a low dwell timer is greater than a definedduration, then the detector threshold is lowered. If a high dwell timeris greater than a defined duration, then the detector threshold isincreased. Of course, in certain embodiments, modes can be implementedsuch that the aforementioned behaviors are overridden or modified. Forinstance, if it is determined that a patient is sleeping, longer dwelltimes can be acceptable without adapting thresholds, and so the systemmight be slowed or paused until a new state is detected.

For example, if a controller stops adjusting stimulation for a longperiod of time (e.g. a defined duration), the threshold should beadjusted. A lack of action therefore causes threshold adjustment.Traditionally, where adaptive systems are SOI tracking, the actuationsystem control policy adapts based on crossing a threshold. Embodimentsof robust adaptive system 600 are configured to further adjust thethresholds themselves based on how long the SOI has been stuck in aparticular state.

As depicted, if the current detector state is low and the actuator stateis low, the dwell timer will be increased because the patient is in alow brain state. Further, assuming the respective state does changestate, if the low detection timer gets too long, the detection thresholdcan be lowered such that the threshold is not “low” anymore. Forexample, if the stimulation cannot be turned down enough to go lower,then threshold set by clinician “low” is no longer low enough.Accordingly, the signal previously classified as “low” is more like anormal signal. As an example, a patient may be on a new medicationregime that globally suppresses all signals. Accordingly, normal andabnormal levels are shifted such that the old thresholds are no longeruseful.

Referring to FIG. 8, a block diagram of a system 700 that providesrobust adaptive brain stimulation in view of therapy duty cycle to apatient brain 702 is depicted, according to an embodiment.

In an embodiment, system 700 can have substantially similar componentsto systems 500 and 600, which are not described again here forconciseness. For example, system 700 can operate on clinician input 704and comprises a plurality of sensing contacts 706, a first switch matrix708, a plurality of signals of interest 710 generated according to aplurality of channels and a digital signal processing, a second switchmatrix 712, an adaptive state machine 714, a third switch matrix 718,and a plurality of stimulation contacts 720.

As depicted, system 700 can further include detector thresholdadjustment logic 716 and 717.

Similar to system 600, adaptive state machine 714 is configured toclassify a signal of interest target based upon thresholds defined inthreshold adjustment logic 716 and 717. Adaptive state machine 714 isfurther configured to control a signal of interest using a proportionalcontrol policy. As depicted, threshold feedback can thus be input backto adaptive state machine 714.

In robust adaptive operation, system 700 is robust to SOI thresholdschanging over time, or improperly configured to begin with. Theclinician defines (e.g. input 704) the stimulation amplitude limits fora proportional control policy. The clinician also defines a signal ofinterest (SOI) threshold upon which system 700 acts. In an embodiment,system 700 assumes that the clinician-defined stimulation operatingspace is a clinical constraint, and that system 700 should be adaptingstimulation within this envelope, but isn't adapting.

In embodiments, as part of input 704, the clinician also defines atarget therapy duty cycle. For example, if the clinician would like thepatient to, on average, be receiving the full stimulation amplitude 50%of the time (when adaptive therapy is running, e.g.), embodiments trackthe average stimulation amplitude delivered. Alternately, a targettherapy duty cycle can allow selection between stimulation waveformpatterns delivering more or less energy, or designed for higher or lowerimpact on the SOI. A detector classification of the signal of interestis then slowly incremented, until the underlying SOI is classified suchthat stimulation will adapt at the target duty cycle provided by theclinician. Such operation is effective for both over-classified andunder-classified signals of interest.

More particularly, referring to threshold adjustment logic 716,embodiments are configured to calculate an average actuator output overtime (e.g. a stimulation amplitude). Embodiments are further configuredto compare the average actuator output to a clinician target duty cycle.In an embodiment, a target duty cycle can be “50% of the stimulationtarget”. Other target duty cycles are of course considered.

Accordingly, at 717, if the average actuator output is greater than thetarget duty cycle, then the detector threshold is increased. If theaverage actuator output is less than the target duty cycle, then thedetector threshold is decreased. As depicted, threshold feedback canthus be input back to adaptive state machine 714.

In embodiments, by using a metric that can be constantly calculated orcalculated at a particular rate, the therapy can be appropriatelyreclassified. For example, if a therapy is not tracking withclinician-defined parameter(s), the SOI is incorrect because thepatient's disease has changed, the patient is taking more medication orless medication, etc., the SOI can be can reclassified based on how muchstimulation, for example, is turned on or off

Referring to FIG. 9, a block diagram of a system 800 that providesrobust adaptive brain stimulation in view of actuation speed to apatient brain 802 is depicted, according to an embodiment.

In an embodiment, system 800 can have substantially similar componentsto systems 500, 600, and 700, which are not described again here forconciseness. For example, system 800 can operate on clinician input 804and comprises a plurality of sensing contacts 806, a first switch matrix808, a plurality of signals of interest 810 generated according to aplurality of channels and a digital signal processing, a second switchmatrix 812, an adaptive state machine 814, a third switch matrix 818,and a plurality of stimulation contacts 820.

As depicted, system 800 can further include actuator speed logic 816.

As further depicted, adaptive state machine 814 is configured to controla signal of interest to a desired target a selected actuator based onactuator speed logic 816. Adaptive state machine 814 is furtherconfigured to control a signal of interest using a selected controlpolicy. Accordingly, actuator feedback can thus be input back toadaptive state machine 814.

In robust adaptive operation, system 800 is robust to changingphysiologic response times. That is, if the physiologic response becomesfaster than the actuation system, system 800 increases the actuationsystem speed. In this example, this is achieved by adjusting thestimulation ramp rate to go faster if the SOI is changing more rapidly.The stimulation ramp rates can be within a bounded set defined by theclinician (e.g. input 804).

More particularly, referring to actuator speed logic 816, embodimentsare configured to calculate a rate of change of an SOI as a function ofa detector threshold range. Embodiments are further configured tocalculate a rate of change of an actuator as a function of the actuatorrange. If the rate of change of the SOI is greater than the range ofchange of the actuator, then the stimulation ramp speed is increased.

Accordingly, it is desirable to be able to actuate system 800 at leastas fast as the SOI is changing. Accordingly, in faster physio responseembodiments, if the SOI is changing faster than the actuator is able tomove, then embodiments can change the rate or ramp speed at whichstimulation is increased or decreased.

Embodiments shown herein, particularly with respect to FIGS. 6-9 havevarious feedback loops. For example, referring to FIG. 6, system 500includes both a closed loop feedback loop from patient brain 502 toplurality of sensing contacts 506, as well as a robust actuator feedbackloop from 516 to 514. As described herein, the robust actuator feedbackloop adapts the adaptive closed loop feedback loop (and correspondingoperations, where applicable). Accordingly, while the closed loopfeedback loop is directed towards shorter-term at-issue therapy, therobust actuator feedback loop is directed towards longer-term therapyeffectiveness.

In systems using IMDs, battery and processing usage considerations areoften a key issue. In a feature and advantage, robust adaptive therapyas described herein, while potentially utilizing additional processing,battery, and communication resources (e.g. reconfiguration usingexternal embodiments), realizes a net gain in resources due to lesstherapy output by the IMD.

In an embodiment, a level of detected stimulation can be utilized as aninternal performance measure to adapt the systems and methods describedherein. For example, traditional systems often have trouble withstimulation artifacts (especially those coming from changes instimulation level or stimulation ramping) being sensed along with theSOI and potentially corrupting the SOI. In embodiment, a level ofdetected stimulation artifact can be utilized as an internal performancemeasure to adapt the stimulation level, rate of change, and/or patternto minimize any corruption.

For example, an electrical stimulation artifact can be sensed viahardware and/or software. Once detected, the electrical stimulationartifact can be removed from the SOI prior to the SOI being input to themeasurement system. In another embodiment, the electrical stimulationartifact can sent without removal in the SOI but instead, handled withinthe logic of the control parameter and/or control policy in addition tothe aforementioned methods to effectively step down the various statesin accordance with the electrical stimulation artifact.

It should be understood that various aspects disclosed herein may becombined in different combinations than the combinations specificallypresented in the description and accompanying drawings. It should alsobe understood that, depending on the example, certain acts or events ofany of the processes or methods described herein may be performed in adifferent sequence, may be added, merged, or left out altogether (e.g.,all described acts or events may not be necessary to carry out thetechniques). In addition, while certain aspects of this disclosure aredescribed as being performed by a single module or unit for purposes ofclarity, it should be understood that the techniques of this disclosuremay be performed by a combination of units or modules associated with,for example, a medical device.

In one or more examples, the described techniques may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored as one or more instructions orcode on a computer-readable medium and executed by a hardware-basedprocessing unit. Computer-readable media may include non-transitorycomputer-readable media, which corresponds to a tangible medium such asdata storage media (e.g., RAM, ROM, EEPROM, flash memory, or any othermedium that can be used to store desired program code in the form ofinstructions or data structures and that can be accessed by a computer).

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor” as used herein may refer toany of the foregoing structure or any other physical structure suitablefor implementation of the described techniques. Also, the techniquescould be fully implemented in one or more circuits or logic elements.

What is claimed is:
 1. An adaptive deep brain stimulation systemcomprising: at least one processor and memory operably coupled to the atleast one processor; a measurement subsystem including: a plurality ofsensing contacts, and measurement subsystem instructions that, whenexecuted on the at least one processor, cause the processor to implementa first signal classification algorithm using one or more of theplurality of sensing contacts to generate a first plurality of signalsof interest; an actuation subsystem including: a plurality ofstimulation contacts, and actuation subsystem instructions that, whenexecuted on the at least one processor, cause the processor to providetherapy on one or more of the plurality of stimulation contacts by oneor more actuators according to a first control policy and a firstcontrol parameter; a reconfiguration subsystem including reconfigurationsubsystem instructions that, when executed on the at least oneprocessor, cause the processor to: determine at least one performancemeasure related to the measurement subsystem or the actuation subsystem,detect a change in performance of the at least one performance measureagainst a performance threshold, based on the detecting: generateupdated measurement subsystem instructions, wherein the updatedmeasurement subsystem instructions includes a second signalclassification algorithm or a second plurality of signals of interest,or generate updated actuation subsystem instructions, wherein theupdated actuation subsystem instructions includes a second controlpolicy or a second control parameter, store at least one of the updatedmeasurement subsystem instructions or the updated actuation subsysteminstructions in the memory, wherein the updated measurement subsysteminstructions or the updated actuation subsystem instructions, whenexecuted on the at least one processor, cause the processor to providean updated therapy on the one or more of the plurality of stimulationcontacts.
 2. The adaptive deep brain stimulation system of claim 1,wherein the reconfiguration subsystem is implemented in an implantablemedical device (IMD).
 3. The adaptive deep brain stimulation system ofclaim 1, wherein the reconfiguration subsystem is implemented in anexternal device, wherein the external device is not co-located with animplantable medical device (IMD).
 4. The adaptive deep brain stimulationsystem of claim 1, wherein the at least one performance measure is anactuator state and the performance threshold comprises the actuatorstate having a high value, a low value, or an in-range value.
 5. Theadaptive deep brain stimulation system of claim 4, wherein when theactuator state has a high value, the second control policy recruitsadditional actuators in the priority order: stimulation amplitude,stimulation pulse width, and stimulation electrodes.
 6. The adaptivedeep brain stimulation system of claim 4, wherein when the actuatorstate has a low value, the second control policy removes additionalactuators in the priority order: stimulation electrodes, stimulationpulse width, and stimulation amplitude.
 7. The adaptive deep brainstimulation system of claim 1, wherein the reconfiguration subsystemfurther includes at least one dwell timer, wherein the at least oneperformance measure is a dwell time calculated by the at least one dwelltimer, and the performance threshold is a duration.
 8. The adaptive deepbrain stimulation system of claim 7, wherein the at least one dwelltimer comprises a low dwell timer and a high dwell timer, each of thelow dwell timer and high dwell timer categorized according to: a currentactuator state defined by an actuator state high value, an actuatorstate low value, or an actuator state in-range value against a currentdetector state defined by a current detector state low value, a currentdetector state in-range value, or a current detector state high value.9. The adaptive deep brain stimulation system of claim 8, wherein thesecond signal classification algorithm includes lowering a detectorthreshold when the low dwell timer is greater than a first duration andraising the detector threshold when the high dwell timer is greater thana second duration.
 10. The adaptive deep brain stimulation system ofclaim 1, wherein the at least one performance measure is an averageactuator output, and the performance threshold is a target duty cycle.11. The adaptive deep brain stimulation system of claim 10, wherein thesecond signal classification algorithm includes increasing a detectorthreshold when the average actuator output is greater than the targetduty cycle and decreasing the detector threshold when the averageactuator output is less than the target duty cycle.
 12. The adaptivedeep brain stimulation system of claim 1, wherein the at least oneperformance measure is: a rate of change of at least one of the firstplurality of signals of interest as a function of a detector thresholdrange, and a rate of change of an actuator as a function of an actuatorrange, wherein the performance threshold whether the rate of change ofat least one of the first plurality of signals of interest as a functionof a detector threshold range is greater than the rate of change of anactuator as a function of an actuator range.
 13. The adaptive deep brainstimulation system of claim 12, wherein the second control policyincludes increasing stimulation ramp speed when the performancethreshold is satisfied.
 14. A method for adapting an adaptive deep brainstimulation system including at least one processor and memory operablycoupled to the at least one processor, a measurement subsystem includinga plurality of sensing contacts, and measurement subsystem instructionsthat, when executed on the at least one processor, cause the processorto implement a first signal classification algorithm using one or moreof the plurality of sensing contacts to generate a first plurality ofsignals of interest, and an actuation subsystem including: a pluralityof stimulation contacts, and actuation subsystem instructions that, whenexecuted on the at least one processor, cause the processor to providetherapy on one or more of the plurality of stimulation contacts by oneor more actuators according to a first control policy and a firstcontrol parameter, the method comprising: determining at least oneperformance measure related to the measurement subsystem or theactuation subsystem, detecting a change in performance of the at leastone performance measure against a performance threshold, based on thedetecting: generating updated measurement subsystem instructions,wherein the updated measurement subsystem instructions includes a secondsignal classification algorithm or a second plurality of signals ofinterest, or generating updated actuation subsystem instructions,wherein the updated actuation subsystem instructions includes a secondcontrol policy or a second control parameter, storing at least one ofthe updated measurement subsystem instructions or the updated actuationsubsystem instructions in the memory, wherein the updated measurementsubsystem instructions or the updated actuation subsystem instructions,when executed on the at least one processor, cause the processor toprovide an updated therapy on the one or more of the plurality ofstimulation contacts.
 15. The method for adapting an adaptive deep brainstimulation system of claim 14, wherein the method is implemented in animplantable medical device (IMD).
 16. The method for adapting anadaptive deep brain stimulation system of claim 14, wherein the methodis implemented in an external device, wherein the external device is notco-located with an implantable medical device (IMD).
 17. The method foradapting an adaptive deep brain stimulation system of claim 14, whereinthe at least one performance measure is an actuator state and theperformance threshold comprises the actuator state having a high value,a low value, or an in-range value.
 18. The method for adapting anadaptive deep brain stimulation system of claim 17, wherein when theactuator state has a high value, the method further comprises:recruiting, with the second control policy, additional actuators in thepriority order: stimulation amplitude, stimulation pulse width, andstimulation electrodes.
 19. The method for adapting an adaptive deepbrain stimulation system of claim 17, wherein when the actuator statehas a low value, the method further comprises: removing, with the secondcontrol policy, additional actuators in the priority order: stimulationelectrodes, stimulation pulse width, and stimulation amplitude.
 20. Themethod for adapting an adaptive deep brain stimulation system of claim14, further comprising: calculating a dwell time using a dwell timer,wherein the at least one performance measure is the dwell time, and theperformance threshold is a duration.
 21. The method for adapting anadaptive deep brain stimulation system of claim 20, wherein the at leastone dwell timer comprises a low dwell timer and a high dwell timer, eachof the low dwell timer and high dwell timer categorized according to: acurrent actuator state defined by an actuator state high value, anactuator state low value, or an actuator state in-range value against acurrent detector state defined by a current detector state low value, acurrent detector state in-range value, or a current detector state highvalue.
 22. The method for adapting an adaptive deep brain stimulationsystem of claim 21, wherein the second signal classification algorithmincludes lowering a detector threshold when the low dwell timer isgreater than a first duration and raising the detector threshold whenthe high dwell timer is greater than a second duration.
 23. The methodfor adapting an adaptive deep brain stimulation system of claim 14,wherein the at least one performance measure is an average actuatoroutput, and the performance threshold is a target duty cycle.
 24. Themethod for adapting an adaptive deep brain stimulation system of claim23, wherein the second signal classification algorithm includesincreasing a detector threshold when the average actuator output isgreater than the target duty cycle and decreasing the detector thresholdwhen the average actuator output is less than the target duty cycle. 25.The method for adapting an adaptive deep brain stimulation system ofclaim 14, wherein the at least one performance measure is: a rate ofchange of at least one of the first plurality of signals of interest asa function of a detector threshold range, and a rate of change of anactuator as a function of an actuator range, wherein the performancethreshold whether the rate of change of at least one of the firstplurality of signals of interest as a function of a detector thresholdrange is greater than the rate of change of an actuator as a function ofan actuator range.
 26. The method for adapting an adaptive deep brainsimulation system of claim 25, wherein the second control policyincludes increasing stimulation ramp speed when the performancethreshold is satisfied.